VERDICTA number of clinical risk prediction scores have recently emerged that are currently available and methodologically suitable for use in the community following a potential infection with the SARS-CoV-2 virus causing COVID-19, or people with suspected or confirmed COVID-19. For some scores, there is evidence that testing has been performed. There are also claims that some of these scores have been validated, however there is limited evidence currently available in the public domain. There are also a number of existing scores that may be suitable that were not developed for, or in response to COVID-19. Further evidence needs to emerge around the validity of all of these scores in patients with COVID-19 before recommendations on their use can be appropriately made.

BACKGROUNDHaving the ability to determine the risk of adverse outcomes for people in the community following a potential infection with the SARS-CoV-2 virus may be useful as part of an exit strategy out of the current government imposed restrictions in the United Kingdom (UK). Further, being able to determine the risk of adverse outcomes for people with suspected or confirmed COVID-19 in the community may be helpful in their clinical management, as well as the wider management of resources across healthcare organisations in their locality.

A recent systematic review by Wynants et al. (2020)1 presented information on prediction models for diagnosis and prognosis of COVID-19 up to the 24th of March 2020, concluding that the proposed models are poorly reported and at a high risk of bias. Our specific focus in this rapid review was to provide a quick reference summary of prognostic clinical risk prediction scores that are currently available in an accessible format and methodologically suitable for use in the community following a potential infection with the SARS-CoV-2 virus causing COVID-19, or people with suspected or confirmed COVID-19.

CURRENT EVIDENCEA pragmatic, non-systematic search of the literature and online sources was performed on the 14th of April 2020. PubMed2 and the Google3 search engine were used.

Clinical risk prediction scores were selected based on meeting the following inclusion criteria:

Clinical risk prediction scores using data that could be collected by health care professionals (HCPs) in the community (i.e. demographics, comorbidities, vital signs etc. Parameters derived from blood or other substances were excluded due to the unavailability of relevant equipment/tests in the community);

Clinical risk prediction scores that were presented in an accessible format for use in clinical practice (i.e. a web application, equation etc).

Identified clinical risk prediction scores were stratified into two categories:

New scores developed specifically for, or in response to, COVID-19;

Existing scores that could be applied to COVID-19.

New scores developed for or in response to COVID-19

Table 1 presents a quick reference summary of the clinical risk prediction scores identified specifically for, or in response to COVID-19 (see the text for further details).

*Whether the score was developed using data from people with COVID-19 **The total sample size used in the development of the score, excluding testing or validation

Coronavirus Mortality Risk Calculator

Patient/near patient contact required: No

Developed: The ‘Coronavirus Mortality Risk Calculator4’ was developed by i5 Analytics. For high volume data processing, a representational state transfer (REST) application programming interface (API) is available for public health authorities and governments.

Summary: Braun et al. (2020)11 described that the ‘Coronavirus Mortality Risk Calculator’ was developed using Artificial Neural Networks (ANN) to calculate the mortality risk for patients infected with influenza or human coronavirus using data from April 2016 to March 2019. The ANN used 50,310 records (medium severity: 22,005; high severity: 27,053; died: 3,216) for training. It was tested on 5,630 records (medium severity: 2,510; high severity: 2,983; died: 327), and had a sensitivity of 80.1% across all three categories resulting in a misclassification error of 19.9%, and specificity of 78.2%. Braun et al. (2020)11 also described that it was validated on 8,423 records (medium severity: 3,755; high severity: 4,463; died: 489), however no results are presented.

Summary: The QxMD website5 described that the ‘COVID-19 Prognostic Tool’ was developed using both Chinese and American data, from the Centers for Disease Control and Prevention (CDC) Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19)12 to calculate mortality risk. The score does not output a single mortality risk that takes into account multiple variables, but rather presents mortality risk separately for the Chinese and American data, in addition to a cardiovascular disease and chronic respiratory disease specific mortality rate from Chinese Data.

The QxMD5 website described that the Chinese data was taken from a report by the Chinese Center for Disease Control and Prevention13. In this report, a total of 44,672 cases were confirmed as COVID-19 (through positive viral nucleic acid throat swab samples). The overall mortality rate was 2.3% (1023 deaths in 44,672 confirmed cases).

Summary: DeCaprio et al. (2020)15 described that the ‘COVID-19 Vulnerability Index’ was developed using machine learning methods applied to an anonymised 5% sample of Medicare claims data from 2015 and 2016 to determine the risk of vulnerability to severe complications from COVID-19. The models used 1,481,654 records for training, and 369,865 for testing. A gradient boosted trees model produced an area under the receiver operating curve (AUROC) of 0.81, and a sensitivity of 0.231 and 0.327 at a 3% and 5% alert rate, respectively.

Shi et al. (2020)7 describe that the ‘Risk Score’ was developed to establish a scoring system to identify people at risk of developing severe symptoms. A total of 487 patients with COVID-19 were included in analysis, with 438 mild (89.9%) and 49 (10.1%) severe cases at admission. In a multivariate analysis, elder age, male sex, and presence of hypertension were independently associated with severe disease at admission, and a risk score was developed using these variables which outputs a value from 0-3. The authors validated the risk score on 66 patients and found that 8.3% of people with a score of 0, 13.8% with a score of 1, 38.9% with a score of 2, and 42.9% with a score of 3 went on to develop severe symptoms.

Publication: None (a paper has been submitted and is under review as of the 15th of April 2020).

Summary: The Surgisphere website16 described that the ‘Surgisphere Mortality Risk Tool’ was developed using data from Surgisphere’s real time global research network comprised of more than 1,200 healthcare organisations. A total of 6,103 patients with polymerase chain reaction (PCR) confirmed COVID-19 infection were evaluated, of which outcomes (survival vs. death) were known in 4,296 patients.

Publication: None (a paper has been submitted and is under review as of the 15th of April 2020).

Summary: The Surgisphere website18 described that the ‘Surgisphere Triage Decision Support Tool’ was developed using data from Surgisphere’s real time global research network comprised of more than 1,200 healthcare organisations. More than 10,000 patients with PCR-confirmed COVID-19 infection were evaluated. The score outputs a category of either ‘Critical’ (Immediate medical attention is required. There is a high risk of major morbidity and death), ‘Urgent’ (Evaluate promptly by a physician. There is a high likelihood that further treatment will be needed to avoid morbidity and possible mortality), or ‘Routine’ (Regularly monitor for signs of progression in a controlled environment. Additional medical attention may be needed). The score correctly classifies 95.5% of patients with regard to urgency of care. Patients with critical symptoms who required immediate medical attention were correctly classified 100% of the time (positive predictive value (PPV) and negative predictive value (NPV)). Patients in the urgent vs. routine categories were correctly classified approximately 94% of the time. The Surgisphere website18 described that the model has been prospectively validated, and that details will be made available along with a copy of the final manuscript once it is accepted for publication.

Existing scores

In addition to new scores developed specifically for, or in response to COVID-19, some existing scores may be useful for patients with suspected/confirmed COVID-19.

NICE guideline (NG 165)19 also suggests that the use of the ‘NEWS222’ score in the community for predicting the risk of clinical deterioration may be useful, although it has not been validated in patients with COVID-19.

A further tool/model which may be useful is ‘qSOFA23’, which identifies high-risk patients for in-hospital mortality with suspected infection outside of the Intensive Care Unit, although it has not been validated in patients with COVID-19.

CONCLUSIONS

Seven clinical risk prediction scores were identified that are currently available and methodologically suitable for use in the community. Some of these scores have evidence that testing has been performed. Some of these scores claim that validation has been performed, however limited evidence was available in the public domain.

Three existing clinical risk prediction scores were identified that are currently available and methodologically suitable for use in the community. These were not developed for, or in response to COVID-19, and therefore require validation in people with COVID-19.

Further evidence needs to emerge around the validity of all of these scores in patients with COVID-19 before recommendations on their use can be appropriately made.

Disclaimer: the article has not been peer-reviewed; it should not replace individual clinical judgement and the sources cited should be checked. The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health and Social Care. The views are not a substitute for professional medical advice.

SEARCH TERMSA pragmatic, non-systematic search of the literature and online sources was performed on the 14th of April 2020. PubMed2 and the Google3 search engine were used.

The following terms were used in PubMed2 to search for relevant literature in the title and abstract: “(’risk’) AND ((‘tool’) OR (’model’) OR (‘score’)) AND (‘COVID-19’)”. A systematic review by Wynants et al. (2020)1 on prediction models for diagnosis and prognosis of COVID-19 searched up to the 24th of March 2020, so we restricted our search results between the 24th of March 2020 and 14th of April 2020, using the paper as a resource for literature and some online resources before the 24th of March 2020.

The following terms were used to search Google3 to form the basis of searching for relevant online resources: “COVID-19 clinical risk prediction model”, “COVID-19 clinical risk prediction tool”, “COVID-19 risk score”, “COVID-19 risk prediction”, “COVID-19 predictive tool” and “COVID-19 predictive model”. Links to other online resources from the main search results were followed where potentially relevant.

Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020.